As AI developers increasingly look to workflow technologies to perform complex integrations of individual software components, there is a growing need for the workflow systems to have expressive descriptions of those components. They must know more than just the types of a component’s inputs and outputs; instead, they need detailed characterizations that allow them to make fine-grained distinctions between candidate components and between candidate workflows. This paper describes PROCAT, an implemented ontology-based cata log for components, conceptualized as processes, that captures and communicates this detailed information. PROCAT is built on a layered representation that allows reasoning about processes at varying levels of abstraction, from qualitative con straints reflecting preconditions and effects, to quantitative predictions about output data and performance. PROCAT employs Semantic Web technologies RDF,OWL, and SPARQL, and builds on SemanticWeb services research.We describe PROCAT’S approach to representing and answering queries about processes, discuss some early experiments evaluating the quantitative predictions, and report on our experience using PROCAT in a system producing workflows for intelligence analysis.